Question 1

Your company uses Kubecost to manage and allocate Kubernetes cloud costs for multiple teams working within a shared cluster. Recently, the FinOps team noticed unexpected cost increases in certain namespaces. They want to identify the primary cost drivers and adjust settings to better control future expenses. Which of the following actions in Kubecost would provide the most comprehensive insights into cost spikes and help optimize resource usage?

Options :

A : Set up cost alerts to notify the FinOps team when a namespace exceeds its daily cost limit.

B : Examine the "Cost Allocation" view to analyze per-namespace costs and allocate a budget cap to prevent overspending.

C : Use the "Savings Recommendations" feature to identify underutilized resources across namespaces and adjust requests and limits accordingly.

D : Configure resource quotas on each namespace to restrict CPU and memory usage and prevent cost spikes.

Answer: C

Question 2

Your company has recently expanded its cloud footprint to include multiple Azure and AWS accounts across different departments. The FinOps team has decided to implement CloudCheckr for unified cost analysis and optimization. However, the team is unsure how to handle the differences in reserved instance (RI) purchasing options between Azure and AWS to maximize cost savings across both platforms. What would be the best approach to optimize reserved instance purchases using CloudCheckr in a multi-cloud setup?

Options :

A : Use CloudCheckr's Reserved Instance (RI) Planner to evaluate AWS and Azure purchases separately to account for different pricing and terms.

B : Allocate all resources under a single department’s account to simplify RI purchases across both platforms.

C : Set up cross-cloud reservations using CloudCheckr to ensure RI discounts apply across both Azure and AWS workloads.

D : Rely on AWS Savings Plans as a substitute for RIs in both AWS and Azure to avoid dealing with separate RI management processes.

Answer: A

Question 3

Your organization has a significant number of underutilized virtual machines (VMs) across multiple cloud environments, resulting in unnecessary expenses. The team aims to develop an automation solution to identify and handle underutilized VMs. Which solution best balances cost optimization, compliance, and operational continuity for underutilized resources?

Options :

A : Create an automated script that identifies underutilized VMs based on specific usage metrics and tags them. These tagged VMs are terminated at the end of the month.

B : Develop a script that detects underutilized VMs based on predefined metrics, schedules them for shutdown during non-peak hours, and sends regular reports to the team for review.

C : Use a script to analyze VM utilization, downscale all VMs to the smallest instance size if they remain idle for 24 hours, and notify team members daily.

D : Write a script to automatically shut down any VM that has been idle for more than 48 hours and alert the team after termination.

Answer: B

Question 4

When managing cloud discounts and rebates in large-scale enterprise environments, which of the following strategies can help maximize savings and ensure cost efficiency?

(Choose two)

Options :

A : Negotiating enterprise-wide volume discounts across all services, regardless of usage patterns.

B : Utilizing commitment-based discounts (e.g., Savings Plans, Enterprise Agreements) for both steady-state and variable workloads.

C : Aligning purchase commitments for reserved instances and savings plans with predictable workloads.

D : Prioritizing only pay-as-you-go pricing for flexible workloads to maintain complete budget flexibility.

Answer: B,C

Question 5

A FinOps team is building a custom integration to aggregate and analyze cloud spend from AWS, Azure, and GCP. They aim to create a unified dashboard for real-time cost insights across all cloud environments. The team needs to decide on the appropriate tools and configurations for data retrieval and aggregation to support both historical and real-time analysis. Which two of the following approaches should they take to ensure reliable, scalable cost data ingestion and analysis?

Options :

A : Set up AWS Lambda functions to retrieve cost data from AWS Cost Explorer API and store it in Amazon RDS for continuous historical and real-time analysis.

B : In GCP, configure the Billing Export to BigQuery for detailed cost information, then build custom SQL queries to extract real-time cost trends and export these summaries to a centralized reporting platform.

C : Leverage Amazon Athena with the AWS Cost and Usage Report (CUR) to perform SQL-based analysis on AWS costs directly, using Athena to feed this data into a centralized dashboard for cross-cloud cost analysis.

D : Use Azure Data Factory (ADF) to schedule and automate the ingestion of data from Azure Cost Management API, storing the data in a centralized data lake such as Azure Data Lake or AWS S3.

Answer: B,D

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